基于多类别生产状态的烧结矿转鼓指数预测模型

将烧结生产大数据与机器学习算法相结合,提出了一种多类别生产状态下预测烧结矿转鼓指数的研究方法.通过数据采集、整合及预处理操作,共获得特征参数65种.以烧结终点位置(BTP)为基础,采用试验研究及可视化分析等方法将转鼓指数划分为2个类别.基于分类别转鼓指数数据集,使用特征选择算法计算了特征参数的重要排序,确定最佳特征参数...

Predictive model of sinter drum index based on multi-category production status

This paper combined big data of sintering production with machine learning algorithms, and proposed a research method for predicting sinter drum index under multi-category production conditions. Through collection, integration and pre-processing operations of data, a total of 65 characteristic parameters were obtained. Based on the sintering end point (BTP), the drum index was divided into two categories used experimental research and visual analysis. Based on the classification of drum index data set, the feature selection algorithm was used to calculate the important ranking of feature parameters, and the best combination of feature parameters was determined as the model input parameters. The LightGBM and CatBoost algorithms were used to establish the prediction models of drum index respectively. The test results showed that the CatBoost prediction model had the best comprehensive prediction effect. Compared with drum index prediction model constructed by all data sets, the prediction effects of abnormal and normal drum index prediction models constructed by categories had been improved to a certain extent. The R2 fit degree could reach 88.09% and 90.69%. In addition, the prediction model of sinter drum index under multi-category production state could achieve a hit rate of 95% within the error range of 0.25%.